Risk Factors for Clinical Coronary Heart Disease in Systemic Lupus Erythematosus: The Lupus and Atherosclerosis Evaluation of Risk (LASER) Study
Bibliographic record
Abstract
OBJECTIVE: Accelerated atherosclerosis and premature coronary heart disease (CHD) are recognized complications of systemic lupus erythematosus (SLE), but the exact etiology remains unclear and is likely to be multifactorial. We hypothesized that SLE patients with CHD have increased exposure to traditional risk factors as well as differing disease phenotype and therapy-related factors compared to SLE patients free of CHD. Our aim was to examine risk factors for development of clinical CHD in SLE in the clinical setting. METHODS: In a UK-wide multicenter retrospective case-control study we recruited 53 SLE patients with verified clinical CHD (myocardial infarction or angina pectoris) and 96 SLE patients without clinical CHD. Controls were recruited from the same center as the case and matched by disease duration. Charts were reviewed up to time of event for cases, or the same "dummy-date" in controls. RESULTS: SLE patients with clinical CHD were older at the time of event [mean (SD) 53 (10) vs 42 (10) yrs; p < 0.001], more likely to be male [11 (20%) vs 3 (7%); p < 0.001], and had more exposure to all classic CHD risk factors compared to SLE patients without clinical CHD. They were also more likely to have been treated with corticosteroids (OR 2.46; 95% CI 1.03, 5.88) and azathioprine (OR 2.33; 95% CI 1.16, 4.67) and to have evidence of damage on the pre-event SLICC damage index (SDI) (OR 2.20; 95% CI 1.09, 4.44). There was no difference between groups with regard to clinical organ involvement or autoantibody profile. CONCLUSION: Our study highlights the need for clinical vigilance to identify modifiable risk factors in the clinical setting and in particular with male patients. The pattern of organ involvement did not differ in SLE patients with CHD events. However, the higher pre-event SDI, azathioprine exposure, and pattern of damage items (disease-related rather than therapy-related) in cases suggests that a persistent active lupus phenotype contributes to CHD risk. In this regard, corticosteroids and azathioprine may not control disease well enough to prevent CHD. Clinical trials are needed to determine whether classic risk factor modification will have a role in primary prevention of CHD in SLE patients and whether new therapies that control disease activity can better reduce CHD risk.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".